Evaluating the Needs and Characteristics of Individuals of Low Socioeconomic Status Using Digital Health Technology to Address Health-Related Social Needs: Mixed Methods Study With Patients and Care Providers
Bibliographic record
Abstract
BACKGROUND: Social determinants of health (SDOH) are the conditions in which people are born, grow, live, work, and age, encompassing social and economic factors that shape health outcomes. There is an increasing call to leverage digital health technology (DHT) to address SDOH and health-related social needs and establish connections to resources and services. OBJECTIVE: This study aimed to (1) identify the DHT-related characteristics of DHT users with low socioeconomic status (SES), (2) determine the needs and preferences of DHT users with low SES, and (3) explore how current SDOH-DHT addresses these needs and preferences in addressing their health-related social needs. METHODS: We used a multiphase, mixed method, user-centered design approach. In phase 1, we developed a user profile based on a literature review, aggregate data, interviews with 26 low-SES individuals, and focus groups with 28 professionals. In phase 2, we conducted a landscape analysis of 17 existing SDOH-DHTs. RESULTS: DHT users of low SES had diverse social and technology characteristics. Five key themes emerged regarding user needs and preferences: (1) user-centered design, including multilingual support, visual guidance, and customization; (2) efficient, solution-based assessment of social risks, assets, and needs; (3) e-caring support features; (4) user education and feedback mechanisms; and (5) trust, privacy, and security. The landscape analysis revealed that current SDOH-DHT features do not adequately meet these needs. CONCLUSIONS: Discrepancies between target user needs and current DHT features represent missed opportunities in developing user-centered tools for individuals of low SES. Findings underscore the importance of inclusive, empowering, and responsive design in SDOH-DHT to bridge health disparities and advance public health.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".